Leveraging CNN and IoT for Effective E-Waste Management

TL;DR

This paper presents an IoT-enabled system utilizing lightweight CNNs and sensor data to automate e-waste identification and sorting, aiming to improve recycling efficiency and reduce environmental impact.

Contribution

It introduces a novel integrated IoT and CNN-based framework for real-time e-waste classification using visual and weight data.

Findings

Effective real-time detection of e-waste components

Enhanced sorting accuracy for recycling processes

Potential for improved waste management workflows

Abstract

The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency.